Soil moisture estimation over agricultural region using time series multi-mode synthetic aperture radar imagery

Efficient monitoring of soil moisture is essential for evaluating crop stress and forecasting agricultural yields. In this regard, synthetic aperture radar (SAR) is considered more suitable than traditional optical satellites due to its
all-weather and all-day capacity, as well as its sensitivity to soil dielectric constant. To further enhance the accuracy of soil moisture retrieval, this project proposes new algorithms based on the change detection method
using multi-mode SAR imagery. The aim is to generate high-precision soil moisture maps instead of relying solely on discrete monitoring points. Additionally, the performance of multi-mode SAR for soil moisture retrieval is
evaluated, offering a valuable reference for selecting data for soil moisture inversion in agricultural areas.

Faculty Supervisor:

Jinfei Wang

Student:

Partner:

A & L Canada Laboratories Inc.

Discipline:

Earth science

Sector:

Agriculture; Professional, scientific and technical services

University:

The University of Western Ontario

Program: